Yulia Tsvetkov
- Artificial Intelligence top 1%
- Experimental and Cognitive Psychology top 5%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 5%
- Sociology and Political Science top 10%
- Co-authors
- Chris DyerXiaochuang HanShuly WintnerAlan W. BlackAnatole GershmanManaal FaruquiKeita KuritaNidhi Vyas
- Topics
- Natural Language Processing Techniques (58 papers)Topic Modeling (55 papers)Hate Speech and Cyberbullying Detection (15 papers)
- Journals
- Proceedings of the National Academy of SciencesComputational LinguisticsJournal of Artificial Intelligence Research
- Partner nations
- United StatesIsraelChina
In The Last Decade
Yulia Tsvetkov
87 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 1.4k
- Experimental and Cognitive Psychology 174
- Information Systems 159
- Computer Vision and Pattern Recognition 154
- Sociology and Political Science 145
Countries citing papers authored by Yulia Tsvetkov
This map shows the geographic impact of Yulia Tsvetkov's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yulia Tsvetkov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yulia Tsvetkov more than expected).
Fields of papers citing papers by Yulia Tsvetkov
This network shows the impact of papers produced by Yulia Tsvetkov. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yulia Tsvetkov. The network helps show where Yulia Tsvetkov may publish in the future.
Co-authorship network of co-authors of Yulia Tsvetkov
This figure shows the co-authorship network connecting the top 25 collaborators of Yulia Tsvetkov. A scholar is included among the top collaborators of Yulia Tsvetkov based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yulia Tsvetkov. Yulia Tsvetkov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 9 | |
| 5 | 8 | |
| 6 | 13 | |
| 7 | 3 | |
| 8 | 22 | |
| 9 | 8 | |
| 10 | 2 | |
| 11 | 24 | |
| 12 | 53 | |
| 13 | 7 | |
| 14 | 7 | |
| 15 | 6 | |
| 16 | 8 | |
| 17 | 18 | |
| 18 | Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | 3 |
| 19 | 44 | |
| 20 | 1 |
About Yulia Tsvetkov
Yulia Tsvetkov is a scholar working on Artificial Intelligence, Health Informatics and General Social Sciences, having authored 96 papers that have together received 1.7k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (58 papers), Topic Modeling (55 papers) and Hate Speech and Cyberbullying Detection (15 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Health Informatics (34 citations) and General Social Sciences (70 citations). Yulia Tsvetkov has collaborated with scholars based in United States, Israel and China. Frequent co-authors include Chris Dyer, Xiaochuang Han, Shuly Wintner, Alan W. Black, Anatole Gershman, Manaal Faruqui, Keita Kurita, Nidhi Vyas, David Jurgens and Dan Jurafsky. Their work appears in journals such as Proceedings of the National Academy of Sciences, Computational Linguistics and Journal of Artificial Intelligence Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.